Capturing the underlying distribution in meta-analysis: Credibility and tolerance intervals
| Autor: | Andrew M. Kiselica, Hannah R. Rothstein, Kimberly A. French, Nenad Apostoloski, Michael T. Brannick |
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| Rok vydání: | 2020 |
| Předmět: |
education.field_of_study
Population Prediction interval Estimator Sample (statistics) 01 natural sciences Education 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Meta-Analysis as Topic Sample size determination Strictly standardized mean difference Statistics Credible interval Confidence Intervals Computer Simulation 030212 general & internal medicine Tolerance interval 0101 mathematics education Monte Carlo Method Mathematics |
| Zdroj: | Research synthesis methodsREFERENCES. 12(3) |
| ISSN: | 1759-2887 |
| Popis: | Tolerance intervals provide a bracket intended to contain a percentage (e.g., 80%) of a population distribution given sample estimates of the mean and variance. In random-effects meta-analysis, tolerance intervals should contain researcher-specified proportions of underlying population effect sizes. Using Monte Carlo simulation, we investigated the coverage for five relevant tolerance interval estimators: the Schmidt-Hunter credibility intervals, a prediction interval, two content tolerance intervals adapted to meta-analysis, and a bootstrap tolerance interval. None of the intervals contained the desired percentage of coverage at the nominal rates in all conditions. However, the prediction worked well unless the number of primary studies was small ( |
| Databáze: | OpenAIRE |
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